The rise of the Internet has occasioned two disparate phenomena: the increase in the presence of social networks, with their corresponding member profiles visible to large numbers of people, and the increase in use of social networks for job searches, both by applicants and by employers. Employers, or at least recruiters attempting to connect applicants and employers, often perform searches on social networks to identify candidates who have qualifications that make them good candidates for whatever job opening they are attempting to fill. The employers or recruiters then can contact these candidates to see if they are interested in applying for the job opening.
Traditional querying of social networks for candidates involves the employer or recruiter entering one or more search terms to manually create the query. A key challenge in talent searches is to translate the criteria of a hiring position into a search query that leads to desired candidates. To fulfill this goal, the searcher has to understand which skills are typically required for the position, what the alternatives are, which companies are likely to have such candidates, from which schools the candidates are most likely to have graduated, and so forth. Moreover, the knowledge varies over time. As a result, it is not surprising that even for experienced recruiters, it often requires many searching trials in order to obtain a satisfactory query.
One specific problem that can occur is that traditional querying typically involves utilizing keyword searching, and when multiple keywords are provided generally it is desirable to locate search results that contain any of the keywords in order to increase the likelihood that a desired result is obtained. This can in some instances, however, cause results that have little relevance to the original query to be retrieved, especially in the employment field. For example, a search on job listings for the terms “machine learning” may result in job listings involving construction (e.g., “will need to be familiar with how to operate heavy machines”) as well as job listings involving education (e.g., “teacher needed for intensive learning school”) that have nothing to do with the artificial intelligence “machine learning” that the searcher intended.
It is desirable to retrieve all documents that are relevant to a query (high recall) to allow users to explore as many relevant jobs as possible and/or allow recruiters to explore as many potential candidates as possible, but it is also desirable to retrieve only documents that are relevant to the query (high precision). Embarrassingly bad search results may show up in top positions of search results when the number of relevant results is low. Furthermore, because facets used by users to filter search results may be ranked by count, retrieval of large numbers of irrelevant jobs may cause facets presented to the searcher to be irrelevant as well.